The statements in this section merely provide background information related to the present disclosure and do not constitute prior art.
Recently, college entrance exams and scholastic ability tests of enterprises are changing in order to evaluate actual English proficiency in speaking or writing.
That is, the Ministry of Education (MOE), in order to enhance proficiency in English, has developed the National English Ability Test (NEAT) (an Internet-based listening, reading, speaking, and writing assessment). The inventor(s) has noted that NEAT is currently enforced as an example, and the civil service exam or Scholastic Aptitude Test (SAT) English test may be replaced with NEAT. In such an English ability test, an automated writing evaluation system is introduced to evaluate writing ability.
The automated writing evaluation system grammatically analyzes a written sentence and evaluates whether the written sentence is grammatically appropriate through error detection. The inventor(s) has noted that in this case, in the process of analyzing the written sentence, a process of analyzing morphemes of the sentence and tagging parts of speech to the morphemes is indispensably required. Because a morpheme analyzing and part-of-speech tagging device of the related art depends upon only part-of-speech sequence information without also considering vocabulary, parts of speech, meanings, and contextual co-occurrence relations of peripheral words, the inventor(s) has experienced that there is a problem in that accuracy is significantly degraded.
The inventor(s) has noted that to solve this problem, an error detection and correction method of complementarily applying rule information and statistical information of words using a dictionary, a language model, and the like, and constructing peripheral word rules and word context information has been proposed.
The inventor(s), particularly, has noted that the language model represents a connection relation between words based on grammatical statistical information using a probability, easily extract the connection relation from many text sentences of a given region, and have high accuracy in error detection. However, actual language naturally varies with time and place rather than obeying standardized rules, and thus often differs from the grammatical statistical information. For example, implied words, Internet terminology, newly established modern language, and the like, violate grammar, but is often used in real life and is detected as errors based on grammatical statistical information.
Accordingly, the inventor(s) has noted that in writing evaluation, there is need of an approach capable of minimizing errors in error detection by applying patterns frequently used by people nowadays, even when a complex rule is not continuously generated, and accurately correcting detected errors.